Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Li, You; Wang, Weixi; Li, Xiaoming; Xie, Linfu; Wang, Yankun; Guo, Ruifeng; Xiu, Wenqun; Tang, Shengjun

doi:10.3390/rs11242920

Cited by 14 publications

(7 citation statements)

References 52 publications

(122 reference statements)

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“…Due to their invariance under different seasons and weather changes, pole-like objects have become high-quality landmarks for vehicle localization in autonomous driving. To make full use of this reliable and long-lasting feature, pole extraction has aroused great interest in academia, especially in the research field of autonomous vehicle navigation and high-definition map (HD map)-making [ 40 , 41 , 42 ]. In Song et al [ 43 ], a point cloud is clustered, and then features such as eigen values, principle components of each cluster, are computed.…”

Section: Methodsmentioning

confidence: 99%

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Liu

Chang

Niu

et al. 2020

Sensors

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Vision-based sensors such as LiDAR (Light Detection and Ranging) are adopted in the SLAM (Simultaneous Localization and Mapping) system. In the 16-beam LiDAR aided SLAM system, due to the difficulty of object detection by sparse laser data, neither the grid-based nor feature point-based solution can avoid the interference of moving objects. In an urban environment, the pole-like objects are common, invariant and have distinguishing characteristics. Therefore, it is suitable to bring more robust and reliable positioning results as auxiliary information in the process of vehicle positioning and navigation. In this work, we proposed a scheme of a SLAM system using a GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and LiDAR sensor using the position of pole-like objects as the features for SLAM. The scheme combines a traditional preprocessing method and a small scale artificial neural network to extract the pole-like objects in environment. Firstly, the threshold-based method is used to extract the pole-like object candidates from the point cloud, and then, the neural network is applied for training and inference to obtain pole-like objects. The result shows that the accuracy and recall rate are sufficient to provide stable observation for the following SLAM process. After extracting the poles from the LiDAR point cloud, their coordinates are added to the feature map, and the nonlinear optimization of the front end is carried out by utilizing the distance constraints corresponding to the pole coordinates; then, the heading angle and horizontal plane translation are estimated. The ground feature points are used to enhance the elevation, pitch and roll angle accuracy. The performance of the proposed navigation system is evaluated through field experiments by checking the position drift and attitude errors during multiple two-min mimic GNSS outages without additional IMU motion constrain such as NHC (Nonholonomic Constrain). The experimental results show that the performance of the proposed scheme is superior to that of the conventional feature point grid-based SLAM with the same back end, especially in congested crossroads where slow-moving vehicles are surrounded and pole-like objects are rich in the environment. The mean plane position error during two-min GNSS outages was reduced by 38.5%, and the root mean square error was reduced by 35.3%. Therefore, the proposed pole-like feature-based GNSS/IMU/LiDAR SLAM system can fuse condensed information from those sensors effectively to mitigate positioning and orientation errors, even in a short-time GNSS denied environment.

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Section: Methodsmentioning

confidence: 99%

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Liu

Chang

Niu

et al. 2020

Sensors

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“…Guan et al, (2015) removed ground points and extracts tree point clouds based on voxel region growing, and segments the point cloud into individual trees by Euclidean distance-based clustering and voxel normalization. Li et al, (2019) based on the observation that the street trees and other objects have different 3D density characteristics, used vertical region growing algorithm to segment the street tree and adjacent ground objects. Guan et al, (2016) extracted clusters by Euclidean distance-based clustering, and realizes the segmentation of adjacent street trees based on voxel normalized cutting.…”

Section: Street Tree Extraction and Segmentationmentioning

confidence: 99%

Street Tree Information Extraction and Dynamics Analysis From Mobile Lidar Point Cloud

Liu

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Street trees are common features and important assets in urban scenes. They are huge in numbers and are constantly changing, thus are difficult to monitor on a regular basis. A method of automatic extraction and dynamic analysis of street trees based on mobile LiDAR data is proposed. First, ground and low objects are filtered from the point clouds. Then, based on a geometric tree model and semantic information, each tree point cloud is extracted, and geometrical parameters such as location, trunk diameter, trunk structure line, tree height, crown width, and crown volume of each tree is obtained. A dynamic analysis combined with the growing characteristics of trees is conducted to compare and analyse the street trees from different epochs, in order to understand whether the trees have grown or been pruned, replanted, or displaced. The proposed algorithm was tested on three epochs of mobile LiDAR data, obtained in 2010, 2016 and 2018, respectively. Experimental results showed that the proposed method was able to accurately detect trees and extract tree parameters for detailed dynamics analysis.

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“…On the other hand, certain articles have studied methods to efficiently segment different road elements that allow a more precise knowledge of the environment to be obtained. Some focus on the extraction of road markings [28][29][30][31][32][33], others on curbs [34][35][36], traffic signs [37][38][39][40], or on the determination of road cracks [41,42], among others.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data

et al. 2022

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The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.

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Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Cited by 14 publications

References 52 publications

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Street Tree Information Extraction and Dynamics Analysis From Mobile Lidar Point Cloud

Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data

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